Optimization of magnetic field design for Hall thrusters based on a genetic algorithm

Magnetic field design is essential for the operation of Hall thrusters.This study focuses on utilizing a genetic algorithm to optimize the magnetic field configuration of SPT70.A 2D hybrid PIC-DSMC and channel-wall erosion model are employed to analyze the plume divergence angle and wall erosion rate,while a Farady probe measurement and laser profilometry system are set up to verify the simulation results.The results demonstrate that the genetic algorithm contributes to reducing the divergence angle of the thruster plumes and alleviating the impact of high-energy particles on the discharge channel wall,reducing the erosion by 5.5%and 2.7%,respectively.Further analysis indicates that the change from a divergent magnetic field to a convergent magnetic field,combined with the upstream shift of the ionization region,contributes to the improving the operation of the Hall thruster.

LSDA-APF:A Local Obstacle Avoidance Algorithm for Unmanned Surface Vehicles Based on 5G Communication Environment

In view of the complex marine environment of navigation,especially in the case of multiple static and dynamic obstacles,the traditional obstacle avoidance algorithms applied to unmanned surface vehicles(USV)are prone to fall into the trap of local optimization.Therefore,this paper proposes an improved artificial potential field(APF)algorithm,which uses 5G communication technology to communicate between the USV and the control center.The algorithm introduces the USV discrimination mechanism to avoid the USV falling into local optimization when the USV encounter different obstacles in different scenarios.Considering the various scenarios between the USV and other dynamic obstacles such as vessels in the process of performing tasks,the algorithm introduces the concept of dynamic artificial potential field.For the multiple obstacles encountered in the process of USV sailing,based on the International Regulations for Preventing Collisions at Sea(COLREGS),the USV determines whether the next step will fall into local optimization through the discriminationmechanism.The local potential field of the USV will dynamically adjust,and the reverse virtual gravitational potential field will be added to prevent it from falling into the local optimization and avoid collisions.The objective function and cost function are designed at the same time,so that the USV can smoothly switch between the global path and the local obstacle avoidance.The simulation results show that the improved APF algorithm proposed in this paper can successfully avoid various obstacles in the complex marine environment,and take navigation time and economic cost into account.

Path Planning for AUVs Based on Improved APF-AC Algorithm

With the increase in ocean exploration activities and underwater development,the autonomous underwater vehicle(AUV)has been widely used as a type of underwater automation equipment in the detection of underwater environments.However,nowadays AUVs generally have drawbacks such as weak endurance,low intelligence,and poor detection ability.The research and implementation of path-planning methods are the premise of AUVs to achieve actual tasks.To improve the underwater operation ability of the AUV,this paper studies the typical problems of path-planning for the ant colony algorithm and the artificial potential field algorithm.In response to the limitations of a single algorithm,an optimization scheme is proposed to improve the artificial potential field ant colony(APF-AC)algorithm.Compared with traditional ant colony and comparative algorithms,the APF-AC reduced the path length by 1.57%and 0.63%(in the simple environment),8.92%and 3.46%(in the complex environment).The iteration time has been reduced by approximately 28.48%and 18.05%(in the simple environment),18.53%and 9.24%(in the complex environment).Finally,the improved APF-AC algorithm has been validated on the AUV platform,and the experiment is consistent with the simulation.Improved APF-AC algorithm can effectively reduce the underwater operation time and overall power consumption of the AUV,and shows a higher safety.

Stability-Considered Lane Keeping Control of Commercial Vehicles Based on Improved APF Algorithm

Regarding the lane keeping system,path tracking accuracy and lateral stability at high speeds need to be taken into account especially for commercial vehicles due to the characteristics of larger mass,longer wheelbase and higher mass center.To improve the performance mentioned above comprehensively,the control strategy based on improved artificial potential field(APF)algorithm is proposed.In the paper,time to lane crossing(TLC)is introduced into the potential field function to enhance the accuracy of path tracking,meanwhile the vehicle dynamics parameters including yaw rate and lateral acceleration are chosen as the repulsive force field source.The lane keeping controller based on improved APF algorithm is designed and the stability of the control system is proved based on Lyapunov theory.In addition,adaptive inertial weight particle swarm optimization algorithm(AIWPSO)is applied to optimize the gain of each potential field function.The co-simulation results indicate that the comprehensive evaluation index respecting lane tracking accuracy and lateral stability is reduced remarkably.Finally,the proposed control strategy is verified by the HiL test.It provides a beneficial reference for dynamics control of commercial vehicles and enriches the theoretical development and practical application of artificial potential field method in the field of intelligent driving.

Effect of applied electric fields on supralinear dendritic integration of interneuron

Evidences show that electric fields(EFs)induced by the magnetic stimulation could modulates brain activities by regulating the excitability of GABAergic interneuron.However,it is still unclear how and why the EF-induced polarization affects the interneuron response as the interneuron receives NMDA synaptic inputs.Considering the key role of NMDA receptor-mediated supralinear dendritic integration in neuronal computations,we suppose that the applied EFs could functionally modulate interneurons’response via regulating dendritic integration.At first,we build a simplified multi-dendritic circuit model with inhomogeneous extracellular potentials,which characterizes the relationship among EF-induced spatial polarizations,dendritic integration,and somatic output.By performing model-based singular perturbation analysis,it is found that the equilibrium point of fast subsystem can be used to asymptotically depict the subthreshold input–output(sI/O)relationship of dendritic integration.It predicted that EF-induced strong depolarizations on the distal dendrites reduce the dendritic saturation output by reducing driving force of synaptic input,and it shifts the steep change of sI/O curve left by reducing stimulation threshold of triggering NMDA spike.Also,the EF modulation prefers the global dendritic integration with asymmetric scatter distribution of NMDA synapses.Furthermore,we identify the respective contribution of EF-regulated dendritic integration and EF-induced somatic polarization to an action potential generation and find that they have an antagonistic effect on AP generation due to the varied NMDA spike threshold under EF stimulation.

Analytical computation of magnetic field in coil-dominated superconducting quadrupole magnets based on racetrack coils

Currently,three types of superconducting quadrupole magnets are used in particle accelerators:cos 2θ,CCT,and serpentine.However,all three coil configurations have complex spatial geometries,which make magnet manufacturing and strain-sensitive superconductor applications difficult.Compared with the three existing quadrupole coils,the racetrack quadrupole coil has a simple shape and manufacturing process,but there have been few theoretical studies.In this paper,the two-dimensional and three-dimensional analytical expressions for the magnetic field in coil-dominated racetrack superconducting quadrupole magnets are presented.The analytical expressions of the field harmonics and gradient are fully resolved and depend only on the geometric parameters of the coil and current density.Then,a genetic algorithm is applied to obtain a solution for the coil geometry parameters with field harmonics on the order of 10^(-4).Finally,considering the practical engineering needs of the accelerator interaction region,electromagnetic design examples of racetrack quadrupole magnets with high gradients,large apertures,and small apertures are described,and the application prospects of racetrack quadrupole coils are analyzed.

Structure-preserving algorithms for guiding center dynamics based on the slow manifold of classical Pauli particle

The classical Pauli particle(CPP) serves as a slow manifold, substituting the conventional guiding center dynamics. Based on the CPP, we utilize the averaged vector field(AVF) method in the computations of drift orbits. Demonstrating significantly higher efficiency, this advanced method is capable of accomplishing the simulation in less than one-third of the time of directly computing the guiding center motion. In contrast to the CPP-based Boris algorithm, this approach inherits the advantages of the AVF method, yielding stable trajectories even achieved with a tenfold time step and reducing the energy error by two orders of magnitude. By comparing these two CPP algorithms with the traditional RK4 method, the numerical results indicate a remarkable performance in terms of both the computational efficiency and error elimination. Moreover, we verify the properties of slow manifold integrators and successfully observe the bounce on both sides of the limiting slow manifold with deliberately chosen perturbed initial conditions. To evaluate the practical value of the methods, we conduct simulations in non-axisymmetric perturbation magnetic fields as part of the experiments,demonstrating that our CPP-based AVF method can handle simulations under complex magnetic field configurations with high accuracy, which the CPP-based Boris algorithm lacks. Through numerical experiments, we demonstrate that the CPP can replace guiding center dynamics in using energy-preserving algorithms for computations, providing a new, efficient, as well as stable approach for applying structure-preserving algorithms in plasma simulations.

Thermal Properties Reconstruction and Temperature Fields in Asphalt Pavements: Inverse Problem and Optimisation Algorithms

A two-layer implicit difference scheme is employed in the present study to determine the temperature distribution in an asphalt pavement.The calculation of each layer only needs four iterations to achieve convergence.Furthermore,in order to improve the calculation accuracy a swarm intelligence optimization algorithm is also exploited to inversely analyze the laws by which the thermal physical parameters of the asphalt pavement materials change with temperature.Using the basic cuckoo and the gray wolf algorithms,an adaptive hybrid optimization algorithm is obtained and used to determine the relationship between the thermal diffusivity of two types of asphalt pavement materials and the temperature.As shown by the results,the prediction accuracy achievable with this approach is higher than that of the linear model.

NOVEL APPROACH FOR ROBOT PATH PLANNING BASED ON NUMERICAL ARTIFICIAL POTENTIAL FIELD AND GENETIC ALGORITHM

A novel approach for collision-free path planning of a multiple degree-of-freedom （DOF） articulated robot in a complex environment is proposed. Firstly, based on visual neighbor point （VNP）, a numerical artificial potential field is constructed in Cartesian space, which provides the heuristic information, effective distance to the goal and the motion direction for the motion of the robot joints. Secondly, a genetic algorithm, combined with the heuristic rules, is used in joint space to determine a series of contiguous configurations piecewise from initial configuration until the goal configuration is attained. A simulation shows that the method can not only handle issues on path planning of the articulated robots in environment with complex obstacles, but also improve the efficiency and quality of path planning.

Nonlinear Inversion of Potential-Field Data Using an Improved Genetic Algorithm

The genetic algorithm is useful for solving an inversion of complex nonlinear geophysical equations. The multi-point search of the genetic algorithm makes it easier to find a globally optimal solution and avoid falling into a local extremum. The search efficiency of the genetic algorithm is a key to producing successful solutions in a huge multi-parameter model space. The encoding mechanism of the genetic algorithm affects the searching processes in the evolution. Not all genetic operations perform perfectly in a search under either a binary or decimal encoding system. As such, a standard genetic algorithm (SGA) is sometimes unable to resolve an optimization problem such as a simple geophysical inversion. With the binary encoding system the operation of the crossover may produce more new individuals. The decimal encoding system, on the other hand, makes the mutation generate more new genes. This paper discusses approaches of exploiting the search potentials of genetic operations with different encoding systems and presents a hybrid-encoding mechanism for the genetic algorithm. This is referred to as the hybrid-encoding genetic algorithm (HEGA). The method is based on the routine in which the mutation operation is executed in decimal code and other operations in binary code. HEGA guarantees the birth of better genes by mutation processing with a high probability, so that it is beneficial for resolving the inversions of complicated problems. Synthetic and real-world examples demonstrate the advantages of using HEGA in the inversion of potential-field data.

Multi-step Reinforcement Learning Algorithm of Mobile Robot Path Planning Based on Virtual Potential Field

A algorithm of dynamic multi-step reinforcement learning based on virtual potential field path planning is proposed in this paper. Firstly, it is constructed the virtual potential field according to the known information. And then in view of Q learning algorithm of the QekT algorithm, a multi-step reinforcement learning algorithm is proposed in this paper. It can update current Q value used of future dynamic k steps according to the current environment status. At the same time, the convergence is analyzed. Finally the simulation experiments are done. It shows that the proposed algorithm and convergence and so on are more efficiency than similar algorithms.

改进A^(*)算法和人工势场法的路径规划

A^(*)算法存在折线路径多和搜索节点多的问题,人工势场(artificial potential field,APF)法存在局部最优和不可到达的问题,针对两种算法存在的问题进行了研究。利用欧氏距离与投影距离提出一种新的混合式启发函数,依据该函数对A^(*)算法的流程进行改进,减少A^(*)算法的搜索节点,提高搜索效率。利用新A^(*)算法生成的最优节点作为APF算法的局部目标点,辅助机器人摆脱局部最优点;通过加入机器人和目标点的位置关系改进势场函数,修改斥力的增益,优化斥力的生成方向。在改进的基础上将两种算法融合提出一种新的算法,利用APF法的势场函数引导A^(*)算法的搜索。从路径长度、避障效果、迭代次数对改进算法进行对比分析,仿真结果表明,提出的改进算法搜索效率高,实现避障的同时保证计算的路径最优。

An iterative algorithm in potential-field inversion

The problem of potential field inversion can be become that of solving system of linear equations by using of linear processing. There are a lot of algorithms for solving any system of linear equations, and the regularized method is one of the best algorithms. But there is a shortcoming in application with the regularized method, viz. the optimum regularized parameter must be determined by experience, so it is difficulty to obtain an optimum solution. In this paper, an iterative algorithm for solving any system of linear equations is discussed, and a sufficient and necessary condition of the algorithm convergence is presented and proved. The algorithm is convergent for any starting point, and the optimum solution can be obtained, in particular, there is no need to calculate the inverse matrix in the algorithm. The typical practical example shows the iterative algorithm is simple and practicable, and the inversion effect is better than that of regularized method.

经颅磁声电刺激对健康与帕金森病大鼠神经回路中beta振荡的影响

背景:经颅磁声电刺激是一种基于磁声耦合电效应的无创、高精度脑神经聚焦刺激方法,可调节神经节律振荡活动,从而影响大脑的运动、认知等功能。目的:探究经颅磁声电刺激对健康与帕金森病大鼠神经回路中beta振荡的影响。方法:①动物实验:采用随机数字表法将24只Wistar大鼠随机分为4组,每组6只:正常对照组不进行任何干预;正常刺激组大脑前额叶皮质接受经颅磁声电刺激(空间峰值脉冲平均强度为13.33 W/cm^(2),基波频率为0.4 MHz,基波周期数为1000,脉冲重复频率为200 Hz);模型对照组、模型刺激组通过腹腔注射1-甲基-4-苯基-1,2,3,6-四氢吡啶建立帕金森病模型,造模成功后,模型对照组大脑前额叶皮质接受经颅磁声电假刺激,模型刺激组大脑前额叶皮质接受经颅磁声电刺激,每天刺激时长2.0 min,刺激结束停留8-10 min后,采集大鼠执行T迷宫过程中局部场电位信号并同时记录行为学正确率,对比分析各组局部场电位信号能量的时频分布情况和行为差异,当大鼠正确率连续3 d高于80%后停止刺激实验和T迷宫实验。②建模仿真实验:构建经颅磁声电刺激下皮质-基底神经节回路模型,分别改变超声发射周期(5,10,20 ms)、超声发射占空比(30%,50%,90%)和感应电流密度(20,50,100μA/cm^(2)),比较不同刺激参数下健康与帕金森病大鼠beta振荡的功率谱密度值。结果与结论:①动物实验:正常对照组大鼠的空间学习能力强于模型对照组(P<0.001),正常刺激组大鼠的空间学习能力强于正常对照组(P<0.05),模型刺激组大鼠的空间学习能力强于模型对照组(P<0.01);正常对照组beta振荡能量分布较为集中,正常刺激组较正常对照组beta振荡信号能量有所降低,模型对照组与模型刺激组beta振荡能量广泛分布且能量值显著高于正常对照组、正常刺激组,并且模型刺激组beta振荡信号能量明显低于模型对照组;②建模仿真实验:不加刺激时,健康大鼠beta频段功率谱密度峰值(30 dB)显著低于帕金森病大鼠(55 dB);施加经颅磁声电刺激后,两组大鼠beta频段功率谱密度值普遍降低;beta频段功率谱密度峰值与超声发射周期呈正相关、与感应电流密度呈负相关,当超声发射占空比为50%时功率谱密度峰值最低;③结果表明:经颅磁声电刺激可抑制健康与帕金森病大鼠的beta振荡,进而改善大鼠的运动功能与决策认知功能。

基于改进人工势场法的AUV全局路径规划

目的为了解决AUV(autonomous underwater vehicle)在传统人工势场法路径规划过程中存在的碰撞、目标不可达和局部极小值等问题,方法提出一种基于改进人工势场法(improved artificial potential field,IAPF)的AUV全局路径规划算法。首先为解决引力过大导致的碰撞问题,引入作用阈限制目标点的吸引力;然后在斥力场添加距离修正因子修正斥力函数,防止目标点离障碍物过近时,由于斥力大于引力引起目标不可达现象,同时考虑到障碍物大小的不等性会影响斥力的作用范围,对障碍物的最小安全距离进行修改;最后在此基础上添加外力,利用外力和势场力的共同作用使AUV沿切线方向行驶,从而摆脱局部极小点或陷阱区域。结果仿真实验结果表明,本文算法能够规划出一条最优路径,与同类算法相比,本文算法运行时间短,成功率高。结论本文提出的基于改进人工势场法的AUV全局路径规划算法,通过引入作用阈、距离修正因子、最小安全距离修正以及外力作用,有效解决了传统人工势场法在AUV路径规划中存在的碰撞、目标不可达和局部极小值等问题。

基于改进APF-FMT^(*)的农业机器人路径规划算法

【目的】解决农业机器人在复杂农业环境下全局路径规划耗时过长、路径最优解求解困难的问题。【方法】提出一种基于改进人工势场法的快速行进树算法(APF-FMT^(*))。首先,在引力势场中引入相对距离,根据与目标点的距离改变引力大小,克服了人工势场法距离目标点过远时引力过大的问题;然后,将FMT^(*)算法与改进人工势场法相结合,采用三阶B样条曲线对路径进行平滑处理;最后,建立3个农业工作地图进行仿真试验。【结果】仿真结果表明,与FMT^(*)、RRT^(*)和Informed-RRT^(*)3种算法对比,在地图Map1和Map2中,APF-FMT^(*)都能快速找到良好的解,且随样本数量增加获得的路径解得到改善,搜索时间比其他3种算法减少45%以上;在有狭小通道的Map3中,APF-FMT^(*)、FMT^(*)搜索时间比RRT^(*)和Informed-RRT^(*)减少75%以上,并且获得更好的解。【结论】本研究提出的APF-FMT^(*)算法不仅克服了FMT^(*)算法冗余探索问题,还有效地解决了人工势场法目标点不可达的问题,提高了农业机器人路径规划效率和作业安全性。

基于AIS轨迹和改进蚁群算法的船舶航线规划方法

在保证船舶航线安全的前提下,以最短航程为目标,提出基于AIS轨迹和改进蚁群算法的船舶航线规划方法。对船舶AIS数据进行预处理,去除船舶AIS数据中的冗余数据,完成船舶AIS数据提纯;采用基于粒子群与K均值混合聚类算法的核心转向点筛选与识别方法,筛选并识别船舶AIS数据中船舶航线核心转向点数据;通过基于改进蚁群算法的航线规划方法,以核心转向点数据为基础,构建航线网络,在此网络中,通过人工势场法对蚁群算法进行改进,对船舶航线进行寻优,实现船舶航线规划。经实验验证,本文方法能够规划出安全合理的船舶航线。

多机器人协同路径的时域协作-势场A*规划

为了减小多机器人协同工作路径长度并提高路径平滑度,提出了基于时域协作-势场A*算法的多机器人协同规划方法。建立了机器人工作环境的栅格模型,分析了传统A*算法辅助信息不充分的问题。为了充分利用环境中的障碍物和目标点位置等先验信息,基于势场法将障碍物和目标点信息引入到A*算法辅助信息中,提出了基于势场A*算法的单机器人路径规划方法。使用三维时空图代替二维平面图,可以有效判断出多机器人碰撞问题,并根据碰撞类型制定了避撞策略。经验证,势场A*算法规划的路径长度和平滑度均优于传统A*算法、文献[11]蚁群算法;在多机器人协同路径规划中,在规划出各机器人最优路径基础上,可以有效解决机器人之间的碰撞问题。实验结果验证了时域协作-势场A*算法在多机器人协同路径规划中的有效性和优越性。